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Gibbs sampling
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131	Better Optimism By Bayes: Adaptive Planning with Rich Models Arthur Guez Gatsby Unit, UCL Add to Reading List Source URL: www.gatsby.ucl.ac.uk Language: English - Date: 2014-06-23 05:46:32 Bayesian network Thompson sampling Reinforcement learning Gibbs sampling Prior probability Hyperprior Estimation theory Supervised learning Statistics Bayesian statistics Bayesian inference
132	PIMS Distinguished Lecture Series Poster - Prepress Add to Reading List Source URL: probability.ca Language: English - Date: 2009-05-07 12:58:46 Computational statistics Probability and statistics Markov chain Monte Carlo Markov chain Gibbs sampling Randomness Algorithm Statistics Markov models Monte Carlo methods
133	CHAPTER ONE. Introduction. My Ph.D. thesis is concerned with convergence properties of certain Markov chains. Specifically, I have studied various random walks on finite and compact Lie groups (Chapters 2 and 3), and ha Add to Reading List Source URL: probability.ca Language: English - Date: 2006-10-04 21:23:47 Markov processes Markov chain Random walk Gibbs sampling Stationary distribution Markov chain Monte Carlo Stationary process Statistics Stochastic processes Markov models
134	Rates of Convergence for Data Augmentation on Finite Sample Spaces by Jeffrey S. Rosenthal School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA (Appeared in Annals of Applied Probability), 8 Add to Reading List Source URL: probability.ca Language: English - Date: 2006-10-04 20:51:05 Markov chain Completeness Gibbs sampling Expectation–maximization algorithm Fisher information Statistics Statistical theory Estimation theory
135	Rates of Convergence for Gibbs Sampling for Variance Component Models by Jeffrey S. Rosenthal Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 1A1 Phone: (. Add to Reading List Source URL: probability.ca Language: English - Date: 2006-10-04 21:24:18 Markov chain Monte Carlo Markov chain Bayesian inference Normal distribution Variance Statistics Markov models Gibbs sampling
136	Bayesian IV: the normal case with multiple endogenous variables Timothy Cogley and Richard Startz SeptemberAbstract Add to Reading List Source URL: www.econ.ucsb.edu Language: English - Date: 2012-09-17 18:42:01 Regression analysis Simultaneous equations model Gibbs sampling Instrumental variable Errors-in-variables models Conjugate prior Multivariate normal distribution Normal distribution Marginal likelihood Statistics Econometrics Bayesian statistics
137	CHAPTER SIX. Concluding Comments. In this thesis, we have considered various Markov chains, and in particular their rates of convergence. Many of the results presented suggest possible generalizations and directions for Add to Reading List Source URL: probability.ca Language: English - Date: 2006-10-04 21:23:36 Markov processes Stochastic processes Markov chain Random walk Gibbs sampling Shuffling Markov chain Monte Carlo Statistics Probability and statistics Markov models
138	A∗ Sampling Chris J. Maddison Dept. of Computer Science University of Toronto Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2015-01-26 08:34:54 Monte Carlo methods Slice sampling Poisson processes Gumbel distribution Gibbs sampling Exponential distribution Estimation theory Order statistic Rejection sampling Statistics Markov chain Monte Carlo Non-uniform random numbers
139	CS-TR-4956 UMIACS-TRLAMP-TR-153 June 2010 Add to Reading List Source URL: www.cs.umd.edu Language: English - Date: 2011-01-03 11:18:10 Monte Carlo methods Markov models Markov chain Monte Carlo Estimation theory Markov chain Expectation–maximization algorithm Gibbs sampling Importance sampling Bayesian inference Statistics Probability and statistics Bayesian statistics
140	Supplement to ”Atmospheric inverse modeling with known physical bounds: an example from trace gas emissions” Scot M. Miller, Anna M. Michalak, and Patricia J. Levi This supplement describes in greater detail the mult Add to Reading List Source URL: www.geosci-model-dev.net Language: English - Date: 2014-12-04 04:35:21 Probability and statistics Gibbs sampling Metropolis–Hastings algorithm Realization Normal distribution Prior probability Multiple-try Metropolis Markov chain Monte Carlo Statistics Monte Carlo methods

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